nov 1 - 2 2005: review meetingacclimate formation flying: a transition opportunity for blakchawks...

Nov 1-2 2005: Review Meeting ACCLIMATE

Formation Flying: a Transition Opportunity for Blakchawks and Other

Rotorcrafts

Hoam Chung and Shankar SastryRobotics and Intelligent Machines Laboratory

University of California, Berkeley


Overview

Formation flight is the primary movement technique for helicopter teams

Formation flight automation can achieve:Diminishing aircrew stressMaintaining a formation under harsh battlefield conditionsOrganized management of

Formation joining/break upIn-flight formation changeScheduling of flight courseEmergency break up


Overview

The algorithm for an autonomous helicopter formation should be able to deal with

Maintaining a stable formation under disturbances

Mixed vehicle formations

Rejoin/breakup with guaranteed safety


Review: Mesh Stability

MotivationSmall disturbance can be amplified through the formation if information of neighboring vehicles is solely used

In order to allow large formations, this algorithm uses “leader information” as well as information from neighbors

Mesh StabilityDamping out error/disturbance propagation in the formation using leader information

Mesh stability is guaranteed for homogeneous formations



Structure of mesh controller

Controller gainK(s)

Spacing errorw.r.t neighbors

Controller gainK(s)

Controller gainK(s)

Spacing errorw.r.t leader

+

+

Autonomously controlledunmanned helicopter

This achieves:



Virtual leader running on a laptop

Real RUAVs

Virtual followers running on laptops

neighbor

neighbor



Animation by A. Pant and X. Xiao



e33

Gap errors are not damped out due to heterogeneityThe vehicle 33 is more agile than others

Using ‘leader information’ results in a dilemma



The use of leader information improves the performance of the autonomous formation flight

For a heterogeneous mesh, an extension of mesh stability theory should be considered

The proposed mesh stability scheme has only one directional information flow

It is not easy to incorporate various objectives in mesh stability scheme

“Mesh Stability” does not mean the “Safety”

It’s a starting point for autonomous formation flight


Inputs from 160th SOAR

In many practical situations, a helicopter team is formed by heterogeneous vehicles

(ex. Little Birds + Blackhwaks + Chinooks)

In-flight manual formation joining process is extremely dangerous

Autonomous aerial refueling will be a great help

The effects of battlefield stress exerted on aircrew increases dramatically under tight formations and in adverse circumstances


Model Predictive Control

Computes control inputs using real-time optimization

Shows better performance than non-predictive controls

Can consider input/state constraints in on-line manner

Easily incorporates various control objectivesRelative gap maintenance

Tracking reference velocities and heading


Decentralized MPC

Gap error definitions: constant gap vs. varying gap

t

n

x yt

n

xy


Decentralized MPC

Finite-horizon optimal control (FHOC) problem

gap error tracking state input


Decentralized MPC

Simulation setupsRight-echelon 8-vehicle-formationInitial gaps are 30 ft. in x,y and zYamaha R-50 linear dynamics plus nonlinear kinematics model used

17 state variables, 4 inputsforward cruise condition, 30mi/h

DynOpt package is used for solving FHOC problemOnly current neighbors’ state variables are interchangedExtrapolate neighbors’ states for prediction

0

1

2

j

7

wind gust on vehicle 0


0 5 10 15 20 25 30 35 40-2.5

-2

-1.5

-1

-0.5

0

0.5

Decentralized MPC

0 5 10 15 20 25 30 35 4014

16

18

20

22

24

26

28

30

32

gap

erro

rs in

x d

irect

ion

(ft)

Time (sec)

gap error 0-1gap error 1-2gap error 2-3gap error 3-4gap error 4-5gap error 5-6gap error 6-7

wind gust induced acceleration (ft/s^2)

0 -2

The maximum relative gap errors are damped

out successfully


Decentralized MPC

Scaling using Froude number (Mettler 2003)Ratio of inertia to gravitational forces

Dynamically similar if models have close Froude numbers

V: characteristic velocity (rotor tip speed)L: characteristic length (rotor radius)

Rotor Radius (ft) Rotor Speed (rad/s) Scale Froude Number

R22 13 53 0.38 1134

Virtual Model

10 62 0.5 1194

R-50 5 89 1 1230

Scale factor


Decentralized MPC

Vehicles 1,2,4,5,and 6 are replaced with scaled-up

virtual model

0 5 10 15 20 25 30 35 4014

16

18

20

22

24

26

28

30

32

gap

erro

r (f

t)

gap error 0-1gap error 1-2gap error 2-3gap error 3-4gap error 4-5gap error 5-6gap error 6-7

0

1

2

j

7

The maximum relative gap errors are damped out successfully also in the case of heterogeneous formation


Formation Manager

Autonomous formation should be reactiveDuring a formation flight, each vehicle may faces various situations

A vehicle in a formation have multiple modes

A high level agent on top of MPC can make autonomous formation safer and more flexible

Formation ManagerScheduling normal breakup/rejoin

Managing course changes and in-flight reconfiguration

Dealing with emergency situations

Managing communication channels


Formation Manager

Overall system structure

FormationManager

MPCController

controlinput

Helicopter

ReferencesRelative gaps

OperatorCommands

Navigation infofrom neighbors

Modified Reference

Vehicle states

Mode


Formation Manager

Inside of the formation manager

Modified Reference

Vehicle states

Finite State Machine

Operator command

s

TrajectoryInterpolator

Navigation infofrom

neighbors

WaypointsReference velocity/headingvalues

MPCTrajectoryGenerator

Mode


Formation Manager

Simple FSM for emergency break up/rejoin

Normal

NormalAs the last follower

FormationAway from the formation

Normal

Single

Approach to the formation

Escape

Sufficientspacing

Rejoiningrequested

Gap errorsmall enough


Formation Manager

Simulations

a. Normal formation b. Proximity warning c. Escape from the formation

d. Fly away from the formation e. Ready to approach f. Rejoin the formation


Conclusions

Without any explicit disturbance rejection algorithm, the proposed MPC based formation shows mesh stable property even for heterogeneous formation

MPC with the proposed formation manager can deal with various formations and many practical issues

Break up/rejoinIn-flight reconfiguration

For safer and more versatile autonomous formation, a formation manager should be implemented as a high level agent


Future Works

Currently, this research is supported by Phase I of ARO STTR with Scientific Systems Company, Inc.

In Phase II, the proposed MPC-based formation flight will be implemented on our BEAR fleet and a series of flight experiments will be performed

Ursa Major 1 Ursa Magna 1


Future Works

The formation manager will be modeled as a hybrid system using hybrid CAD tools like HyVisual

(Part of) formation manager functionality will be implemented on-board and in-flight reconfiguration will be demonstrated

Technology transition to Blackhawk:

Human operator interaction is another future research subject

nov 1 - 2 2005: review meetingacclimate formation flying: a transition opportunity for blakchawks...

Documents

heterogeneous mesh

mesh stabilityvirtual

mesh stabilityanimation

stable formation

autonomous formation

mesh stabilitygap errors

t leader

directional information